Model Server for PyTorch Documentation

Basic Features

• Serving Quick Start - Basic server usage tutorial

• Model Archive Quick Start - Tutorial that shows you how to package a model archive file.

• Installation - Installation procedures

• Serving Models - Explains how to use torchserve.

  • REST API - Specification on the API endpoint for TorchServe

• Packaging Model Archive - Explains how to package model archive file, use model-archiver.

• Logging - How to configure logging

• Metrics - How to configure metrics

• Batch inference with TorchServe - How to create and serve a model with batch inference in TorchServe

• Model Snapshots - Describes how to use snapshot feature for resiliency due to a planned or unplanned service stop

Advanced Features

• Advanced settings - Describes advanced TorchServe configurations.

• Custom Model Service - Describes how to develop custom inference services.

• Unit Tests - Housekeeping unit tests for TorchServe.

• Benchmark - Use JMeter to run TorchServe through the paces and collect benchmark data

Default Handlers

• Image Classifier - This handler takes an image and returns the name of object in that image

• Text Classifier - This handler takes a text (string) as input and returns the classification text based on the model vocabulary

• Object Detector - This handler takes an image and returns list of detected classes and bounding boxes respectively

• Image Segmenter - This handler takes an image and returns output shape as [CL H W], CL - number of classes, H - height and W - width